What Sets the Serving Speed of Looped Language Models? Measurements and Optimizations Graded by Exactness
Measures inference bottlenecks in looped language models and improves cache packing, kernel fusion and prefix reuse. Separates changes that preserve exact outputs from changes to batching, randomness or numerical computation.
First public: · Added to this list: November 9, 2026
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.